Mobility Information Engineering Lab at ETH Zürich
Ye Hong gave a talk at the Swiss Transport Research Conference (STRC) 2024 with the title Towards realistic individual activity location demand synthesis using deep generative networks. Reach out if you are interested in the topic and want to learn more!
We had a very interesting and stimulating Workshop on ‘Reproducibility in tracking data analysis and mobility research’ at the ACM SIGSPATIAL 2023 conference in Hamburg. Featuring an excellent keynote by Edzer Pebesma who highlighted the importance of replicability & reproducibility for Spatial Data Science. Check out our website and tutorial materials.
Our new paper entitled “Evaluating geospatial context information for travel mode detection” was accepted at Journal of Transport Geography and is now available (open-access!) online.
How much does geospatial context information contribute to travel mode detection?
Our latest study reveals that geospatial network features, such as distance to the road network, are more critical than motion features, such as speed and acceleration, when classifying an extensive list of travel modes. Still, most land-use and land-cover features barely contribute to the task. The results are based on our extensive context representation reviews and the proposed analytical pipeline to assess the contribution of geospatial context information based on a random forest model and the SHapley Additive exPlanation (SHAP) method.
The study provides valuable guidance for feature selection, effective feature design, and building efficient travel mode detection models.
Check out the paper online and the corresponding code on Github!
Our new paper entitled “Context-aware multi-head self-attentional neural network model for next location prediction” was accepted at Transportation Research Part C: Emerging Technologies and is now available (open-access!) online.
We present a multi-head self-attentional (MHSA) neural network that integrates location features, temporal features, and functional land use contexts for next location prediction. This comprehensive approach effectively captures movement-related spatio-temporal information, leading to state-of-the-art performance on GNSS mobility datasets.
Our analysis demonstrates that training the model on population data yields superior results by learning from collective movement patterns, surpassing the capabilities of individual-level models. Moreover, we emphasize the significance of recent past movements and weekly periodicity, showing that learning from a subset of historical mobility is sufficient to obtain an accurate location prediction result.
The proposed model represents a pivotal advancement in accurate and interpretable individual mobility prediction, and can be readily applied in downstream applications, including planning on-demand transport services, implementing mobility incentives, and suggesting alternative mobility options.
Check out the paper online and the corresponding code on Github!